Update README.md

README.md

@@ -13,8 +13,10 @@ tags:
 - mindcraft
 ---
 
-# 🧠 Andy‑4 🧠
+# 🧠 Andy‑4 ⛏️
 
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/66960602f0ffd8e3a381106a/raWYEDo2An1biTLXd5PfN.png)
 **Andy‑4** is an 8 billion‑parameter specialist model tuned for Minecraft gameplay via the Mindcraft framework.  Trained on a single RTX 3090 over **three weeks**, Andy‑4 delivers advanced reasoning, multi‑step planning, and robust in‑game decision‑making.
 
 > ⚠️ **Certification:**  
@@ -28,9 +30,9 @@ tags:
 - **Training Hardware:** 1 × NVIDIA RTX 3090  
 - **Duration:** ~3 weeks total  
 - **Data Volumes:**  
-  - **Messages:** 179 384  
-  - **Tokens:** 425 535 198  
-  - **Conversations:** 62 149  
+  - **Messages:** 179,384  
+  - **Tokens:** 425,535,198  
+  - **Conversations:** 62,149  
 
 - **Base Architecture:** Llama 3.1 8B  
 - **License:** [Andy 1.1 License](LICENSE)  
@@ -43,14 +45,17 @@ tags:
 1. **Andy‑4‑base‑1** dataset  
    - **Epochs:** 2  
    - **Learning Rate:**   7e-5
+   - **Dataset Size:** 47.4k
 
 2. **Andy‑4‑base‑2** dataset  
    - **Epochs:** 4  
    - **Learning Rate:**   3e-7
+   - **Dataset Size:** 48.9k
 
 3. **Fine‑tune (FT) dataset**  
    - **Epochs:** 2.5  
    - **Learning Rate:** 2e-5
+   - **Dataset Size:** 4.12k
 
 - **Optimizer:** AdamW_8bit with cosine decay  
 - **Quantization:** 4‑bit (`bnb-4bit`) for inference
@@ -60,11 +65,7 @@ tags:
 
 ## 🚀 Installation
 
-### 1. Quick Hugging Face + Ollama *(Not recommended)*
-
-1. On the HF model page, click **Use this model → Ollama**.  
-2. Choose your quantization (see table).  
-3. Copy and run the provided `ollama run` command.
+First, you need to choose your quantization, this chart is with the base of `8192` set as the context window
 
 | Quantization | VRAM Required |
 |--------------|---------------|
@@ -74,9 +75,12 @@ tags:
 | Q3_K_M       | 6 GB (low)    |
 | Q2_K         | 4–6 GB (ultra)|
 
-If you lack a GPU, check the [Mindcraft Discord guide](https://ptb.discord.com/channels/1303399789995626667/1347027684768878644/1347027684768878644) for free cloud setups.
+### 1. Installation directly on Ollama *(Fastest and easiest)*
 
----
+1. Visit [Andy-4 on Ollama](https://ollama.com/Sweaterdog/Andy-4)
+2. Copy the command after choosing model type / quantization
+3. Run the command in the terminal
+4. Set the profile's model to be what you installed, such as `ollama/sweaterdog/andy-4:latest`
 
 ### 2. Manual Download & Modelfile
 
@@ -84,15 +88,6 @@ If you lack a GPU, check the [Mindcraft Discord guide](https://ptb.discord.com/c
    - From the HF **Files** tab, grab your chosen `.GGUF` quant weights (e.g. `Andy-4.Q4_K_M.gguf`).  
    - Download the provided `Modelfile`.
 
-     Follow this table to choose your quantization, this is for a 8192 context window, the default, as well as a non-quantized context window.
-
-| Quantization | VRAM Required |
-|--------------|---------------|
-| F16          | 16 GB+        |
-| Q5_K_M       | 8 GB+         |
-| Q4_K_M       | 6–8 GB        |
-| Q3_K_M       | 6 GB (low)    |
-| Q2_K         | 4–6 GB (ultra)|
 
 2. **Edit**
 
@@ -122,6 +117,9 @@ This registers the **Andy‑4** model locally.
 
 ---
 
+If you lack a GPU, check the [Mindcraft Discord guide](https://ptb.discord.com/channels/1303399789995626667/1347027684768878644/1347027684768878644) for free cloud setups.
+
+
 ## 🔧 Context‑Window Quantization
 
 To lower VRAM use for context windows: